Detecting Special Lecturers Using Information theory-based Outlier Detection Method

Do, Thuc-Doan; Tran, Tuan Minh; Le, Xuan-May Thi; Duong, Thuy-Van T.

doi:10.1145/3093241.3093274

Cited by 1 publication

(1 citation statement)

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“…Geng and Guo (2012) uses association rules mining technique called Apriori algorithm to gauge faculty performance and then provide decision support capabilities for higher management. An information theory-based algorithm was proposed by Do, Tran, Le, and Duong (2017) to detect lectures that are entirely different from other lectures called outliers. Dutt, Aghabozrgi, Ismail, and Mahroeian (2015) discussed various clustering techniques applied on educational data to mine useful patterns.…”

Section: Usesmentioning

confidence: 99%

A Novel Framework Using Machine Learning to Effectively Analyze the Faculty Evaluations

Islam¹

2018

J.educ.soc.sci

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In this paper, a three pronged solution to faculty evaluation is proposed. Almost in every university, faculty and course evaluations are filled by students after the completion of courses. Due to the large volume of such evaluations, it becomes very difficult for management to carefully analyze them. This paper proposes a framework based on machine learning techniques that can be adopted for effective evaluation of faculty. It uses k-means clustering to group the evaluations and points out the specific area on which management needs to work on with faculty. Along with the quantitative evaluation of faculty, students also provide feedback in the form of comments. The proposed solution performs sentiment analysis on those comments. If there is a high emotion (positive or negative) associated with comments, an email can be sent in real-time to higher management. Another important component of proposed solution is providing summary of the topics discussed in the lectures via transcribing their recorded lecture and then applying machine learning on transcripts.

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Section: Usesmentioning

confidence: 99%

A Novel Framework Using Machine Learning to Effectively Analyze the Faculty Evaluations

Islam¹

2018

J.educ.soc.sci

View full text Add to dashboard Cite

show abstract

Detecting Special Lecturers Using Information theory-based Outlier Detection Method

Cited by 1 publication

References 13 publications

A Novel Framework Using Machine Learning to Effectively Analyze the Faculty Evaluations

A Novel Framework Using Machine Learning to Effectively Analyze the Faculty Evaluations

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